Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine
This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow w...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/741 |
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| author | Yue Chen Weili Kou Wenna Miao Xiong Yin Jiayue Gao Weiyu Zhuang |
| author_facet | Yue Chen Weili Kou Wenna Miao Xiong Yin Jiayue Gao Weiyu Zhuang |
| author_sort | Yue Chen |
| collection | DOAJ |
| description | This study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow was developed on Google Earth Engine by combining Dynamic World land cover data with official fire records, utilizing a logistic regression-based feature selection strategy and an enhanced SNIC segmentation GEOBIA framework. The performance of four classifiers (RF, SVM, KNN, CART) in burn detection was evaluated through a comparative analysis of their spectral–spatial discrimination capabilities. The results indicated that the RF classifier achieved the highest performance, with an overall accuracy of 96.32% and a Kappa coefficient of 0.951. Spatial analysis further revealed that regions at medium altitudes (800–1600 m) and moderate slopes (15–25°) are more prone to forest fires. This study demonstrates a robust approach for generating accurate large-scale forest fire maps and provides valuable insights for effective fire management in complex terrain areas. |
| format | Article |
| id | doaj-art-0e69e896c3aa4c04ba44bbd9a8c79012 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-0e69e896c3aa4c04ba44bbd9a8c790122025-08-20T02:59:07ZengMDPI AGRemote Sensing2072-42922025-02-0117574110.3390/rs17050741Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth EngineYue Chen0Weili Kou1Wenna Miao2Xiong Yin3Jiayue Gao4Weiyu Zhuang5College of Forestry, Southwest Forestry University, Kunming 650224, ChinaCollege of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaCollege of Forestry, Southwest Forestry University, Kunming 650224, ChinaThis study aimed to accurately map burned forest areas and analyze the spatial distribution of forest fires under complex terrain conditions. This study integrates Landsat 8, Sentinel-2, and MODIS data to map burned forest areas in the complex terrain of western Yunnan. A machine learning workflow was developed on Google Earth Engine by combining Dynamic World land cover data with official fire records, utilizing a logistic regression-based feature selection strategy and an enhanced SNIC segmentation GEOBIA framework. The performance of four classifiers (RF, SVM, KNN, CART) in burn detection was evaluated through a comparative analysis of their spectral–spatial discrimination capabilities. The results indicated that the RF classifier achieved the highest performance, with an overall accuracy of 96.32% and a Kappa coefficient of 0.951. Spatial analysis further revealed that regions at medium altitudes (800–1600 m) and moderate slopes (15–25°) are more prone to forest fires. This study demonstrates a robust approach for generating accurate large-scale forest fire maps and provides valuable insights for effective fire management in complex terrain areas.https://www.mdpi.com/2072-4292/17/5/741burned forest areasgeographic object-based image analysis (GEOBIA)Sentinel-2Landsat 8MODIS |
| spellingShingle | Yue Chen Weili Kou Wenna Miao Xiong Yin Jiayue Gao Weiyu Zhuang Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine Remote Sensing burned forest areas geographic object-based image analysis (GEOBIA) Sentinel-2 Landsat 8 MODIS |
| title | Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine |
| title_full | Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine |
| title_fullStr | Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine |
| title_full_unstemmed | Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine |
| title_short | Mapping Burned Forest Areas in Western Yunnan, China, Using Multi-Source Optical Imagery Integrated with Simple Non-Iterative Clustering Segmentation and Random Forest Algorithms in Google Earth Engine |
| title_sort | mapping burned forest areas in western yunnan china using multi source optical imagery integrated with simple non iterative clustering segmentation and random forest algorithms in google earth engine |
| topic | burned forest areas geographic object-based image analysis (GEOBIA) Sentinel-2 Landsat 8 MODIS |
| url | https://www.mdpi.com/2072-4292/17/5/741 |
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